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In the rapidly evolving landscape of 5G technology, safeguarding Radio Frequency (RF) environments against sophisticated intrusions is paramount, especially in dynamic spectrum access and management. This paper presents an enhanced experimental model that integrates a self-attention mechanism with a Recurrent Neural Network (RNN)-based autoencoder for the detection of anomalous spectral activities in 5G networks at the waveform level. Our approach, grounded in time-series analysis, processes in-phase and quadrature (I/Q) samples to identify irregularities that could indicate potential jamming attacks. The model's architecture, augmented with a self-attention layer, extends the capabilities of RNN autoen-coders, enabling a more nuanced understanding of temporal dependencies and contextual relationships within the RF spectrum. Utilizing a simulated 5G Radio Access Network (RAN) test-bed constructed with srsRAN 5G and Software Defined Radios (SDRs), we generated a comprehensive stream of data that reflects real-world RF spectrum conditions and attack scenarios. The model is trained to reconstruct standard signal behavior, establishing a normative baseline against which deviations, indicative of security threats, are identified. The proposed architecture is designed to balance between detection precision and computational efficiency, so the LSTM network, enriched with self-attention, continues to optimize for minimal execution latency and power consumption. Conducted on a real-world SDR-based testbed, our results demonstrate the model's improved performance and accuracy in threat detection.
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Software Radio with MATLAB Toolbox for 5G NR Waveform Generation
The main resource for providing wireless services is radio frequency (RF) spectrum. In order to explore new uses of spectrum shared among radio systems and services, field data needs to be collected. In this paper we design a testbed that can generate different 5G New Radio (NR) downlink transmission frames using the MATLAB 5G Toolbox, software-defined radio (SDR) hardware and GNU Radio Companion. This system will be used as a part of a testbed to study the RF interference caused by 5G transmissions to remote sensing receivers and evaluate different mechanisms for co-channel coexistence.
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- Award ID(s):
- 2030291
- PAR ID:
- 10356292
- Date Published:
- Journal Name:
- 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS)
- Page Range / eLocation ID:
- 430 to 433
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/DCOSS54816.2022.00078
